Untitled - socium.ge

Why information should influence productivity 167influence productivity has renewed long-standing debate among economistsover the sources of productivity growth. We argue that the complexity of therelationship between information and productivity necessitates approachesthat transcend traditional disciplinary boundaries and acknowledge contributionsfrom economics, complexity, and network theories.Our argument begins by linking theoretical notions for valuing informationas data and process to the economic definition of total factor productivity.Formally recognizing the economic value of information as process opens thedoor for integrating theory from multiple traditions.A major contribution of this work is to codify predictions of various theoriesand connect them to white-collar productivity. One set of theories considersquestions of value and information as facts. The economic traditionconnects information to output via risk, precision, push, search, standards, andincentives. Another set of theories helps understand efficiency and informationas instructions. Computational and network models connect information tooutput via topological efficiency, modular design, standards, centrality, modeling,and search.While relationships between information and productivity are clearlycomplex, they should be amenable to testing and validation. Along this line,the second contribution of this work is to provide a glimpse of how eachhypothesis might be interpreted and applied. In the specific context of executivesearch, absolute incentive systems track information sharing, larger socialnetworks are observed with more revenues and higher completion rates,routines correlate with revenue, decentralized data entry parallels perceptionsof information control, and centrality seems connected to revenue. Althoughanecdotal in nature, these illustrations from a continuing multi-year studypoint to the means of probing these predictions further.Empirical verification of hypotheses will undoubtedly involve considerableingenuity in generating suitable controls and translating predictions of theoryinto precise measures of information use and human interaction. This processis ongoing. The greater promise, however, lies in the potential not only toreflect on patterns of organization as they exist, but to generate new lines ofresearch that actively informs business practice in light of the opportunitiesoffered by continuing advances in information, network, and communicationtechnologies.ACKNOWLEDGMENTSWe gratefully acknowledge valuable suggestions from Manuel Castells,Michael Cohen, David Croson, Misha Lipatov, Brian Subirana, and JunZhang. Charles King III provided useful resources and helpful conversations.